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Multi-class Classification

Multi-class classification is a type of supervised learning where the goal is to assign an input to one of three or more distinct classes. Unlike binary classification (which has only two classes), multi-class classification handles multiple labels and uses algorithms like logistic regression, decision trees, random forests, SVMs, or neural networks to predict the correct category based on the features of the input data.

Papers

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Showing 521–530 of 903 papers

TitleStatusHype
Semi-supervised binary classification with latent distance learning—0
Semi-supervised Meta-learning for Cross-domain Few-shot Intent Classification—0
Semi-supervised Outlier Detection using Generative And Adversary Framework—0
Semi-Supervised Zero-Shot Classification With Label Representation Learning—0
Sentence-wise Smooth Regularization for Sequence to Sequence Learning—0
Sentiment Analysis for YouTube Comments in Roman Urdu—0
Sequential Binary Classification for Intrusion Detection—0
Sequential Classification with Empirically Observed Statistics—0
Set-valued classification -- overview via a unified framework—0
Set-valued prediction in hierarchical classification with constrained representation complexity—0
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All datasetsCOVID-19 CXR DatasetCOVID chest X-rayReuters-52TII-SSRC-23Training and validation dataset of capsule vision 2024 challenge.

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1COVID-CXNetAccuracy (%)94.2—Unverified
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1COVID-ResNetF1 score0.9—Unverified
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1SVM (tficf)Macro F173.9—Unverified
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1Extra TreesF1-Score93.36—Unverified
#ModelMetricClaimedVerifiedStatus
1Multi-Model EnsembleMean AUC0.99—Unverified